Search Results (4,023)

Near-infrared imagery is essential for vegetation monitoring, precision agriculture, and environmental remote sensing, but multispectral UAV systems remain significantly more expensive and less accessible than conventional RGB imaging platforms. This study presents a lightweight artificial intelligence framework for reconstructing the NIR spectral band exclusively from the red spectral band acquired by a UAV. The proposed methodology formulates the reconstruction task as a pixel-wise nonlinear regression problem and employs a compact multilayer perceptron (MLP) containing only 609 trainable parameters, without exploiting spatial neighborhood information. The framework was developed and evaluated using 280 synchronized multispectral UAV image sets acquired with a DJI Phantom 4 Multispectral platform over a heterogeneous agricultural landscape in the Republic of Moldova. Of these, 252 image sets were used for model development, and 28 were reserved as a held-out within-mission test subset. Quantitative evaluation on a held-out test dataset from the same acquisition mission yielded a mean squared error of 0.010329, a root mean squared error of 0.101632, a mean absolute error of 0.079883, a coefficient of determination of 0.253383, and a Pearson correlation coefficient of 0.683637 between measured and reconstructed normalized NIR digital intensities. The results indicate that the model captures part of the red–NIR relationship under the evaluated acquisition conditions; however, the moderate coefficient of determination suggests that the reconstructed values are an approximation rather than a replacement for measured NIR observations. An illustrative NDVI-based assessment showed that broad spatial vegetation patterns remained identifiable. Rather than introducing a new neural network architecture, this work establishes a compact empirical baseline to investigate the practical performance and limitations of pixel-wise NIR reconstruction from a single red-band value with minimal model complexity. Full article

The increased interest in environmental sustainability, driven by the transition to renewable energies, has spotlighted woodchip as an effective alternative to fossil fuel. Despite being readily available, its natural origin results in inherent heterogeneity, affecting every stage of the supply chain up to the combustion. Firstly, moisture content is detrimental for calorific value, leading to ineffective combustion drawbacks in transport and challenging storage. Water content monitoring along the production chain is crucial but its evaluation with standard analyses is limited due to destructive approach, unsuitable schedules and consequent costs. Near-infrared spectroscopy (NIRS), as an alternative technique, provides a non-destructive, repeatable, and faster method for both lab and inline control, overcoming standards limits. Considering the comparation already run with equal method, NIRS performances in predicting moisture content in Italian industrial woodchip simulating an in-line system are evaluated. Steady collection of continuous replicates improves woodchip representativeness, reducing estimation bias and enhancing prediction reliability (determination coefficient ~0.9, error <3%). Given the qualitative screening results, NIRS shows high water detection ability, acting as portable tool for real-time characterization in power plants. The immediate evaluation of water content could support critical steps along the supply chain, contributing to clean energy and market safety. Full article

Among the various brain–computer interface (BCI) modifications used in post-stroke rehabilitation, BCI systems combined with functional electrical stimulation (FES) are considered the most effective. As a preliminary step toward optimizing such systems for clinical application, it remains unclear whether using electroencephalography (EEG) alone versus a hybrid EEG and functional near-infrared spectroscopy (fNIRS) approach affects real-time three-class BCI–FES control performance in healthy individuals. In a blind randomized study, 16 healthy volunteers completed five BCI–FES training sessions across three days. In one group, FES of wrist extensor muscles was driven by a hybrid EEG–fNIRS classifier; in the other, by EEG only. Classification accuracy, sense of agency, attention, and physical comfort were assessed. No statistically significant between-group differences were found in any outcome measure (p > 0.05). Median real-time three-class classification recall was 53.5% in the hybrid group and 57.3% in the EEG-only group. The median agency score reached approximately 75% of the maximum possible value in both groups. Simulation analysis showed comparable accuracy for unimodal fNIRS-only and EEG-only classifiers. Genetic algorithm-based channel selection identified C3 and C4 as the most informative EEG channels, while optimal fNIRS placement required individual optimization. Within the constraints of the classification and fusion pipeline used here, these findings suggest that signal acquisition modality does not significantly influence BCI–FES performance or sense of agency in healthy subjects. The complete EEG–fNIRS dataset is publicly available through NITRC. Full article

Cassava is the sixth most important food crop globally, valued for its high starch accumulation in tuberous roots, which supports diverse applications in food processing and industrial production. Quality evaluation is vital for its utilization, yet rapid and efficient assessment methods remain underexplored. In this study, 67 cassava varieties were collected to assess their nutritional quality. Near-infrared spectroscopy (NIRS) in the wavenumber range of 11,000–4000 cm⁻¹ was applied to predict key quality traits of cassava flour from different varieties. Combined with partial least squares (PLS), principal component analysis (PCA), and internal cross-validation, a preliminary NIRS-based screening framework for cassava flour quality indicators was established. The protein and moisture models achieved an excellent quantitative prediction performance (${\mathrm{R}}_{\mathrm{c}\mathrm{v}}^2$ > 0.82, RPD > 4.0), qualifying them as reliable tools for routine analysis. In contrast, the models for starch, amylose, and amylopectin showed substantial overfitting with low cross-validation accuracies (${\mathrm{R}}_{\mathrm{c}\mathrm{v}}^2$ = 0.19–0.38), restricting their use to only preliminary screening. The fat model performed poorly (${\mathrm{R}}_{\mathrm{c}\mathrm{v}}^2$ = 0.16, RPD = 1.08), rendering it unsuitable for any quantitative or screening application. This study provides an exploratory screening framework for rapid multi-index evaluation of cassava flour quality. However, the marked variability in predictive performance across constituents highlights the critical need for external validation to improve model robustness. Full article

Coastal zones are subject to a range of natural and anthropogenic processes that result in coastal erosion and accretion, threatening essential infrastructure and straining livelihoods. Analysis of shoreline changes is thus crucial for informing coastal zone planning and management to avert the ramifications of erosion and accretion. Despite a range of literature examining coastline changes worldwide, there is a paucity of literature focusing on Southern Africa, particularly within small harbours. The paper, therefore, aims to analyse shoreline changes at four small harbour zones along the Atlantic Ocean in the Western Cape province, South Africa, over the period from 1985 to 2025. To acquire an accurate shoreline position, four spectral criteria were applied simultaneously: the Automated Water Extraction Index (AWEI), the Modified Normalised Difference Water Index (MNDWI), the Normalised Difference Vegetation Index (NDVI), and the Near Infrared (NIR). Four statistics were then used to measure shoreline changes in the USGS Digital Shoreline Analysis System (DSAS): Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR), and Weighted Linear Regression (WLR). Considerable variability was observed within and among the four small harbour study areas, with several erosion and accretion hotspots identified. The 20-year forecast indicated that future shoreline positions would largely maintain the 2025 curvature. Although the study did not reveal significant threats, authorities are encouraged to pay particular attention to erosion and accretion hotspots through appropriate mitigation and adaptation efforts. Full article

Economically motivated adulteration of powdered ginger with low-cost cereal flours represents an increasing concern for food authenticity and quality control. The aim of this study was to develop and validate a rapid, reliable, and non-destructive method for the quantitative determination of powdered ginger adulteration using visible and near-infrared (Vis–NIR) spectroscopy coupled with chemometric modelling. Ginger powder samples were adulterated with wheat, corn, and rice flours at concentrations ranging from 5 to 50% (w/w), with particular emphasis on the low-to-medium adulteration interval (10–25%), where reliable quantification is especially relevant for food fraud detection. Spectral data acquired in the visible (400–700 nm), near-infrared (700–2500 nm), and combined Vis–NIR (400–2500 nm) regions were preprocessed using Savitzky–Golay filtering and evaluated using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). In addition, Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Random Forest (RF) were compared for sample classification. Among the evaluated approaches, LDA achieved the highest classification accuracy (>95%) using the NIR spectroscopic region, while PLSR models developed from the NIR spectral region provided the best quantitative performance, with validation coefficients of determination above 0.99, prediction errors below 1%, and RPD values greater than 13. The results demonstrate that Vis–NIR spectroscopy combined with chemometric modelling enables accurate discrimination between authentic and adulterated samples, as well as reliable quantification of flour adulteration in powdered ginger without sample preparation or chemical reagents. The proposed methodology constitutes a rapid, environmentally friendly, and cost-effective analytical strategy with strong potential for routine quality control and food fraud prevention. Full article

Bacterial contamination remains a critical safety concern in platelet transfusion, and there is an urgent demand for decontamination technologies that eliminate contaminating bacteria without damaging platelet viability and physiological function. Herein, Fe₃O₄-QCS-PEI-Cu-apt microparticles with enlarged magnetic cores were rationally fabricated to improve aptamer immobilization, aiming at targeted bacterial elimination via near-infrared (NIR) irradiation while maintaining platelet function. Fluorescence assays confirmed their specific targeting capability toward Staphylococcus aureus (S. aureus) without binding to platelets. Magnetic separation experiments demonstrated that the aptamer-functionalized microparticles could efficiently capture and remove 91.35% of S. aureus from platelets. NIR irradiation of the Fe₃O₄-QCS-PEI-Cu core induced marked bactericidal activity against S. aureus in suspension, as determined by plate counting and LIVE/DEAD staining. This antibacterial ability originated from the intrinsic photo-responsive property of the composite rather than from aptamer-mediated recognition, and could be easily extended to bacteria captured by aptamer-functionalized particles. Comprehensive biocompatibility evaluations, including morphological observation, hematological parameter analysis, CD62P expression detection, and thromboelastography (TEG) were performed. And the results demonstrated that there were no significant changes in platelet morphology, count, activation state, or overall hemostatic function, apart from an increase in the α angle. This platform achieves efficient targeted NIR-triggered antibacterial efficacy while maintaining excellent platelet compatibility, offering a promising strategy to enhance the safety of platelet transfusion. Full article

Adulteration and contamination represent an ever-growing global problem, due to the emergence of increasingly sophisticated illicit commercial practices involving high-value and widely consumed products. Traditional chromatographic methods, such as liquid chromatography coupled with mass spectrometry, offer high sensitivity but their application is often limited by long analysis time, complex sample preparation, and the use of non-eco-friendly organic solvents. In contrast, enzymatic and immunoassay-based methods are rapid and require minimal sample preparation, but their applicability may be restricted by antibody specificity and potential cross-reactivity. In this context, near-infrared (NIR) spectroscopy is a rapid, non-destructive technique that does not require the use of solvents and is becoming increasingly relevant in food analysis. This review provides an overview of the potential of this spectroscopic technique, coupled with chemometrics, for the detection of adulterants and contaminants in various matrices. After a brief summary of the principles on which it is based and the instruments that can be used, the chemometric processes useful for data interpretation have been discussed, as well as the main applications in liquid and solid food, including oils, milk, juices, spices, cereals, coffee, and dietary supplements. The analyzed case studies indicated that even very low levels (ppm) of adulterants and contaminants can be detected with high accuracy and sensitivity using models such as PLSR, SVM, RF, and CNNs and new and emerging devices such as portable ones. Full article

Moisture content is an important indicator of grain storage safety, processing quality, and circulation efficiency. However, the performance of near-infrared spectroscopy (NIRS) models is affected by differences in grain crops, sample morphologies, preprocessing strategies, feature selection methods, and regression models, and the adaptation relationships among these factors remain insufficiently understood. In this study, soybean, maize, and wheat samples with whole-grain and powder morphologies were investigated to reveal the method adaptation patterns of NIRS-based moisture prediction under consistent experimental conditions. Spectra in the range of 900–1700 nm were collected, and 54 analytical pathways were established by combining six preprocessing strategies, three feature selection algorithms (SPA, CARS, and UVE), and three regression models (PLSR, SVR, and RF). The results showed that representative optimal pathways achieved Rp values above 0.9730 for powder samples and above 0.9587 for whole-grain samples. Different grain crops and sample morphologies exhibited distinct analytical pathway preferences, indicating that appropriate analytical strategies should be selected according to specific detection objects. Spectral variation analysis further demonstrated higher spectral variability in whole-grain samples than in powder samples. This study provides insights into the selection of suitable NIRS analytical strategies for grain moisture prediction and quality assessment. Full article

Background: Migraine diagnosis and severity staging rest on clinical interviews according to the ICHD-3 criteria, with no validated objective biomarker. Functional near-infrared spectroscopy (fNIRS) is a portable and non-invasive method, but existing fNIRS migraine classification studies remain relatively small and rely on cognitive tasks. We assessed whether prefrontal hemodynamic responses with a head-down-to-knees maneuver separate migraine patients from controls, and high- from low-severity migraine. Methods: Prefrontal fNIRS, including short separation channels, was recorded during the maneuver in 50 interictal migraine patients and 52 controls screened for the absence of migraine, serious chronic conditions and hypertension. Nine hemodynamic parameters per chromophore (HbO, Hb and HbT) across thirty channels entered a hypothesis-neutral pipeline of 270 candidate pipelines (3 chromophores × 3 feature selection strategies × 30 classifiers) with no predefined region of interest. Results: Deoxyhemoglobin features selected by embedded L1 regularization with shrinkage-regularized linear discriminant analysis separated the groups with 92% balanced accuracy on the internal hold-out (92% sensitivity, 92% specificity, ROC-AUC 0.99; permutation p = 3 × 10⁻⁴), against 87% in development-set cross-validation and 88% under nested selection cross-validation, with a selection bias of +0.03. High- vs. low-severity classification (19 high, 31 low) did not exceed chance under nested validation (45%; permutation p = 0.45). Conclusions: A wide-scale pipeline achieved a robust, validated separation of migraine from screened controls, carried by a distributed venous-weighted deoxyhemoglobin signature; specificity against other headache disorders remains to be established. Attack frequency severity was not separable above chance, indicating that scalar hemodynamic descriptors are sufficient for a categorical but not a graded contrast. Full article

Wearable brain-imaging devices have been developed to meet the growing demand in the healthcare industry for long-term monitoring of brain signals in natural conditions, such as monitoring brain diseases and emotions. However, conventional EEG and fNIRS (functional near-infrared spectroscopy) devices are often expensive, bulky and difficult to operate, making it difficult to monitor patients for long periods in natural conditions. To address these issues, this article proposes a low-cost, portable and multimodal wearable brain signal acquisition scheme. It combines EEG (electroencephalography) and fNIRS to reflect brain activity from different perspectives. In order to make it more wearable, a conductive rubber material is used as the electrode for the EEG. In this study, the corresponding experiments were used to verify the performance of the device. The first is the measurement of internal system noise, which satisfies the data acquisition of EEG and fNIRS at different gain levels. The α-rhythm experiment and the SSVEP (steady-state visual evoked potentials) experiment were used to validate the performance of EEG data acquisition. The performance of the fNIRS was verified by measuring changes in cerebral blood oxygen during breath-hold and breathing. In addition, by decomposing the raw fNIRS data with the VMD (variational mode decomposition) algorithm and performing correlation analysis, heart rate information was separated from the data. The performance of the proposed device was validated in the above experiments, confirming the feasibility of the design for multimodal data acquisition and meeting the requirements for portability and wearability. Furthermore, the proposed device was tested with 31 subjects (15 depressive subjects) to detect depression. Experiments proved the effectiveness of the multimodal signals, which outperformed single modal and surpassed EEG by 8.4% and fNIRS by 23.5%. Full article

Transparent glazing systems are increasingly required to provide simultaneous control over light transmission, solar heat gain, thermal losses, surface contamination, and environmental durability, creating new challenges for the development of multifunctional coating technologies. This review critically examines advanced optical and self-cleaning coatings developed for transparent glass and polymeric substrates, with particular emphasis on the relationships between materials chemistry, surface/interface chemistry, microstructure, and functional performance. Dielectric multilayers, metal oxides, ceramic coatings, sol-gel-derived hybrid systems, and emerging chromogenic materials are discussed in terms of their chemical compositions, structural characteristics, and mechanisms governing optical, thermal, and surface properties. Particular attention is given to structure–property relationships associated with photosynthetically active radiation (PAR) transmission, near-infrared (NIR) management, thermal emissivity, solar modulation, wettability, and self-cleaning behavior, together with their implications for energy-efficient transparent glazing and greenhouse environments. The influence of coating architecture, porosity, surface roughness, interfacial interactions, and deposition conditions on functional performance and long-term stability is critically evaluated. The advantages and limitations of representative deposition strategies are further compared, considering scalability, process compatibility, substrate sensitivity, and application to heat-sensitive polymeric films. Environmental degradation mechanisms induced by ultraviolet irradiation, moisture, thermal cycling, and mechanical stresses are analyzed to identify the key factors governing coating durability and sustainability. Finally, current knowledge gaps and emerging research directions are identified, highlighting the need for rational materials design, multifunctional integration, scalable fabrication, and improved structure-property-durability correlations for next-generation transparent glazing and greenhouse applications. Full article

Rapid and non-destructive assessment of sweet potato quality is important for postharvest management, processing suitability evaluation, and market quality control. In this study, a 12-channel visible/near-infrared (Vis/NIR) spectral sensing system was applied to predict multiple physicochemical quality attributes of postharvest sweet potatoes. Sixty independent sweet potato storage roots were measured at three representative positions, producing 180 position-specific observations; measurements from the same root were retained within the same validation group. The measured attributes included dry matter content (DMC), starch content (SC), soluble solids content (SSC), and the CIE 1976 L*a*b* (CIELAB) color coordinates L*, a*, and b*. Four spectral treatment conditions, including original spectra, normalization, standardization, and first-derivative transformation, were combined with partial least squares regression (PLSR), multiple linear regression (MLR), extreme gradient boosting (XGBoost), and random forest (RF), generating 16 prediction strategies for each quality attribute. Root-grouped five-fold cross-validation showed that the optimal models achieved coefficients of determination for cross-validation (R²CV) ranging from 0.9083 to 0.9190 and residual predictive deviation (RPD) values ranging from 3.3112 to 3.5230. Repeated grouped cross-validation produced mean R²CV values of 0.9113–0.9176, and root-block Y-scrambling yielded empirical p values of 0.005 for all six attributes. PLSR provided the highest cross-validated performance for all six quality attributes, although MLR showed comparable performance for several targets. These results provide preliminary evidence that discrete Vis/NIR spectral sensing can support simultaneous non-destructive estimation of multiple sweet potato quality attributes. External multi-batch and multi-cultivar validation is required before the models can be considered robust for practical deployment. Full article

This study evaluates operational optical to near-infrared (NIR) band (400–1050 nm) atmospheric transmittance simulation at the Ali-CPT site (5250 m above sea level). Monthly mean profiles from a 20-year Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis were combined with precipitable water vapor (PWV) constraints from the POM-02 sun photometer and validated against DTF-8 sun photometer measurements under fixed aerosol parameters (visibility of 75 km, Clean Continental mode). Mode 1 used real-time lidar extinction profiles; Mode 2 used built-in aerosol modes scaled by aerosol optical depth (AOD). Both modes achieved correlation coefficients greater than 0.93. Mode 1 showed root mean square errors (RMSEs) of 0.034–0.040 in the 400–870 nm range, while Mode 2 exhibited a systematic negative bias (RMSE 0.040–0.052) due to the mismatch between the fixed visibility assumption and the much cleaner winter conditions. The errors in the 940 nm water vapor absorption band were dominated by the vertical structural deviation of the MERRA-2 water vapor profile during high-PWV summer conditions. The results confirm the feasibility of reanalysis-based operational transmittance simulation at data-sparse high-altitude sites. Full article

Surface wettability is a critical factor influencing the biological performance of orthopedic Ti implants. The native TiO₂ film on Ti undergoes changes in wettability under ultraviolet (UV) irradiation. However, the limited tissue penetration of UV light compared with near-infrared (NIR) light restricts its potential clinical application. In this study, a black TiO₂ (b-TiO₂) coating with NIR-responsive wettability was fabricated directly on a Ti substrate using a one-step atmospheric plasma spraying process. Under 808 nm NIR irradiation, the water contact angle of the b-TiO₂ coating increased from 0° to 154.4 ± 4.0°, indicating a transition from a superhydrophilic to a stable superhydrophobic state. FTIR and XPS analyses showed that NIR-induced photothermal heating promoted the removal of surface hydroxyl groups and the passivation of oxygen-deficient sites, thereby driving the wettability transition. Among TiO₂ coatings with hydrophilic, intermediate-wettability, and hydrophobic surfaces, the hydrophobic coating effectively directed macrophage polarization toward the anti-inflammatory M2 phenotype and delivered slightly superior osteoblast activity. It also markedly inhibited Staphylococcus aureus adhesion, achieving an anti-adhesion efficiency of 98.61%. These findings demonstrate that NIR irradiation can regulate the wettability of plasma-sprayed b-TiO₂ coatings and provide concurrent immunomodulatory and antibacterial effects. This approach may support the development of light-responsive surfaces for orthopedic implants. Full article